In late stage oncology trial design and monitoring, OS and PFS/EFS/DFS are commonly used primary efficacy endpoints. PFS can be surrogate endpoints to predict clinical benefit in metastatic setting and EFS/DFS could be used to support traditional approval in neoadjuvant/adjuvant setting of oncology treatment. Therefore, it is of interest to explore the correlation between PFS (or EFS/DFS) and OS endpoints via realistic and comprehensive simulations. Considering similar parametric assumptions in Fleischer et al. (2009) and Li and Zhang (2015) for two dependent survival endpoints, and motivated by “simtrial” (Anderson, 2020), we developed flexible algorithms can simulate a realistic patient-level dataset, which contains patients’ enrollment and failure/dropout information for the two endpoints. Since non-proportional hazard situation such as delayed treatment effect or cure rate commonly occur in cancer trials, especially for immunotherapy, we also consider two types of cure models accommodating delayed effect change point and piecewise exponential or Weibull distribution in our simulators. The proposed simulation and visualization algorithms are implemented in an R package.